From f9143bd90ee989e7fede640b4c411374be8e2099 Mon Sep 17 00:00:00 2001 From: Benjamin Auder Date: Thu, 23 Mar 2017 15:46:29 +0100 Subject: [PATCH] downgrade roxygen2 to v5.0.1, rename EMGLLF -> EMGLLF_R --- pkg/DESCRIPTION | 20 ++++++++++---------- test/generate_test_data/EMGLLF.R | 2 +- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION index d3841a7..5febdc0 100644 --- a/pkg/DESCRIPTION +++ b/pkg/DESCRIPTION @@ -2,15 +2,15 @@ Package: valse Title: Variable Selection With Mixture Of Models Date: 2016-12-01 Version: 0.1-0 -Description: Two methods are implemented to cluster data with finite mixture regression models. - Those procedures deal with high-dimensional covariates and responses through a variable - selection procedure based on the Lasso estimator. A low-rank constraint could be added, - computed for the Lasso-Rank procedure. - A collection of models is constructed, varying the level of sparsity and the number of - clusters, and a model is selected using a model selection criterion (slope heuristic, - BIC or AIC). Details of the procedure are provided in 'Model-based clustering for - high-dimensional data. Application to functional data' by Emilie Devijver, published in - Advances in Data Analysis and Clustering (2016). +Description: Two methods are implemented to cluster data with finite mixture + regression models. Those procedures deal with high-dimensional covariates and + responses through a variable selection procedure based on the Lasso estimator. + A low-rank constraint could be added, computed for the Lasso-Rank procedure. + A collection of models is constructed, varying the level of sparsity and the + number of clusters, and a model is selected using a model selection criterion + (slope heuristic, BIC or AIC). Details of the procedure are provided in 'Model- + based clustering for high-dimensional data. Application to functional data' by + Emilie Devijver, published in Advances in Data Analysis and Clustering (2016). Author: Benjamin Auder [aut,cre], Emilie Devijver [aut], Benjamin Goehry [aut] @@ -28,4 +28,4 @@ Suggests: URL: http://git.auder.net/?p=valse.git License: MIT + file LICENSE VignetteBuilder: knitr -RoxygenNote: 6.0.1 +RoxygenNote: 5.0.1 diff --git a/test/generate_test_data/EMGLLF.R b/test/generate_test_data/EMGLLF.R index de6d40e..37859d8 100644 --- a/test/generate_test_data/EMGLLF.R +++ b/test/generate_test_data/EMGLLF.R @@ -1,4 +1,4 @@ -EMGLLF = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau) +EMGLLF_R = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau) { #matrix dimensions n = dim(X)[1] -- 2.44.0